A fuzzy neural network approach for online fault detection in waste water treatment process |
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| Affiliation: | 1. System Engineering Department, University of Malaga, Campus de Teatinos s/n, 29071, Malaga, Spain;2. System Engineering Department, University of Malaga, Campus de Teatinos s/n, 29071, Malaga, Spain;3. Dept. of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo, 2, I-09123 Cagliari, Italy;4. Dept. of Civil and Environmental Engineering, Aalto University, P.O. Box 12100, FI-00076 Aalto, Finland;5. Department of Chemical Engineering, Federal University of Campina Grande, 58429-140 Campina Grande, Brazil;6. Centre for Intelligent Systems, Faculty of Science and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Algarve, Portugal;7. System Engineering Department, University of Malaga, Campus de Teatinos s/n, 29071, Malaga, Spain;1. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;3. Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria 3083, Australia |
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| Abstract: | In this paper, an effective strategy for fault detection of sludge volume index (SVI) sensor is proposed and tested on an experimental hardware setup in waste water treatment process (WWTP). The main objective of this fault detection strategy is to design a system which consists of the online sensors, the SVI predicting plant and fault diagnosis method. The SVI predicting plant is designed utilizing a fuzzy neural network (FNN), which is trained by a historical set of data collected during fault-free operation of WWTP. The fault diagnosis method, based on the difference between the measured concentration values and FNN predictions, allows a quick revealing of the faults. Then this proposed fault detection method is applied to a real WWTP and compared with other approaches. Experimental results show that the proposed fault detection strategy can obtain the fault signals of the SVI sensor online. |
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| Keywords: | Fault detection Fuzzy neural network Bulking sludge Waste water treatment process Sludge volume index |
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